Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient&#39;s Heart

ABSTRACT

Disclosed are various examples and embodiments of systems, devices, components and methods configured to detect a location of a source of at least one cardiac rhythm disorder in a patient&#39;s heart. In some embodiments, electrogram signals are acquired from inside a patient&#39;s heart, and subsequently normalized, adjusted and/or filtered, followed by generating a two-dimensional (2D) spatial map, grid or representation of the electrode positions, processing the amplitude-adjusted and filtered electrogram signals to generate a plurality of three-dimensional electrogram surfaces corresponding at least partially to the 2 D grid, one surface being generated for each or selected discrete times, and processing the plurality of three-dimensional electrogram surfaces through time to generate a velocity vector map corresponding at least partially to the 2 D grid. The resulting velocity vector map is configured to reveal the location of the source of the at least one cardiac rhythm disorder, which may be, by way of example, an active rotor in a patient&#39;s myocardium and atrium.

FIELD OF THE INVENTION

Various embodiments described and disclosed herein relate to the fieldof medicine generally, and more particularly to diagnosing and treatingcardiac rhythm disorders in a patient's heart using electrophysiologicalmapping techniques, as well as in some embodiments using imaging,navigation, cardiac ablation and other types of medical systems,devices, components, and methods. Various embodiments described anddisclosed herein also relate to systems, devices, components and methodsfor discovering with enhanced precision the location(s) of the source(s)of different types of cardiac rhythm disorders and irregularities in apatient's heart, such as, by way of example, active rotors, passiverotors, areas of fibrosis, breakthrough points and focus points.

BACKGROUND

Persistent atrial fibrillation (AF) is assumed to be caused bystructural changes in atrial tissue, which can manifest themselves asmultiwavelet re-entry and/or stable rotor mechanisms (see, e.g., DeGroot Miss. et al., “Electropathological Substrate of LongstandingPersistent Atrial Fibrillation in Patients with Structural Heart DiseaseEpicardial Breakthrough,” Circulation, 2010, 3: 1674-1682). Radiofrequency (RF) ablation targeting such host drivers of AF is generallyaccepted as the best therapeutic approach. RF ablation success rates intreating AF cases are currently limited, however, by a lack ofdiagnostic tools that are capable of precisely determining the source(or type), and location, of such AF drivers. Better diagnostic toolswould help reduce the frequency and extent of cardiac ablationprocedures to the minimum amount required to treat AF, and would helpbalance the benefits of decreased fibrillatory burden against themorbidity of increased lesion load.

One method currently employed to localize AF drivers is the TOPERA®RhythmView® system, which employs a basket catheter having 64 electrodesarranged in an 8×8 pattern from which the system records unipolarelectrograms or electrogram signals (EGMs). The RhythmView® algorithmcreates a propagation map of the 64 electrodes through a phase analysisof EGM peaks after improving the signal to noise ratio through filteringand subtraction of a simulated compound ECG artifact. The RhythmView®algorithm detects where peak sequences between electrodes show acircular pattern candidate for a re-entry cycle and indicates thoselocations in a Focal Impulse and Rotor Map (FIRM) using A1 to H8 chessfield coordinates for the electrodes. The resolution of the TOPERAsystem is limited by the spacing of the electrodes and consequently doesnot show the details of the AF drivers. In particular, the TOPERA systemcannot show if a circular EGM wavefront is actively generated by are-entry mechanism and is therefore is a driver of AF (i.e., an activerotor), or whether a circular EGM wavefront simply represents turbulencepassively generated by an EGM wavefront hitting a barrier (i.e., apassive rotor). In addition, the TOPERA system does not show thedirection of AF wavefront propagation, and does not provide the spatialor temporal resolution required to detect singularities associated withthe generation of an active rotor.

A recent independent multicenter study (“OASIS, Impact of Rotor Ablationin Non-Paroxysmal AF Patients: Results from a Randomized Trial,”Sanghamitra Mohanty, et al. and Andrea Natale, J Am Coll Cardiol. 2016)reported that the results obtained using TOPERA FIRM technology wereinferior to those provided by non-specific ablation of the posteriorwall of the left atrium. Moreover, the results suggested that FIRM basedablation is not sufficient for therapeutic success without pulmonaryvein isolation (PVI) being performed in parallel. Although there aresome questions about the methodology of this trial, many experts areconvinced that the resolution and interpretability of the TOPERA systemneed to be improved.

In another approach to the problem, Toronto scientists recentlypresented a strategy to analyze EGM wave propagation using “OmnipolarMapping,” which seeks to measure beat by beat conduction velocity anddirection (see, e.g., “Novel Strategy for Improved Substrate Mapping ofthe Atria: Omnipolar Catheter and Signal Processing Technology AssessesElectrogram Signals Along Physiologic and Anatomic Directions,” D.Curtis Deno et al. and Kumaraswamy Nanthakumar; Circulation. 2015;132:A19778). This approach starts with the time derivative of a unipolarEGM as measured by a set of electrodes having known distances to oneother. Assuming constant velocity, the velocity and directionrepresenting the best fit for a spatial derivative of the measured EGMare calculated and used to represent an estimate of the E field.According to a communication by Dr. Nanthakumar at the 2016 CardioStimConvention in Nice, France, however, this method remains incapable ofdealing successfully with complex data sets, such as those obtainedduring an episode of AF.

What is needed are improved means and methods of acquiring andprocessing intracardiac electrogram signals that reliably and accuratelyyield the precise locations and sources of cardiac rhythm disorders in apatient's heart. Doing so would enable cardiac ablation procedures to becarried out with greater locational precision, and would result inhigher rates of success in treating cardiac rhythm disorders such as AF.

Accordingly, it is one objective of the present invention to provide animproved system, especially a medical system, and methods for acquiringand processing intracardiac electrogram signals that reliably andaccurately yield the precise locations and sources of cardiac rhythmdisorders in a patient's heart.

SUMMARY

In one embodiment, there is provided a system for detecting in apatient's heart a location of a source of at least one cardiac rhythmdisorder, the system comprising at least one computing device comprisingat least one non-transitory computer readable medium configured to storeinstructions executable by at least one processor to perform a method ofdetermining the source and location of the cardiac rhythm disorder inthe patient's heart, the computing device being configured to: (a)receive electrogram signals; (b) normalize or adjust amplitudes of theelectrogram signals; (c) assign predetermined positions of theelectrodes on a mapping electrode assembly to their correspondingelectrogram signals; (c) provide or generate a two-dimensional (2D)spatial map of the electrode positions; (d) for discrete or selectedtimes over which the electrogram signals are being processed, processthe amplitude-adjusted electrogram signals to generate a plurality ofthree-dimensional electrogram surfaces corresponding at least partiallyto the 2D map, one surface being generated for each such time, and (e)process the plurality of three-dimensional electrogram surfaces throughtime to generate a velocity vector map corresponding at least partiallyto the 2D map, the velocity vector map being configured to reveal thelocation of the source of the at least one cardiac rhythm disorder.

In another embodiment, there is provided a method of detecting alocation of a source of at least one cardiac rhythm disorder in apatient's heart, the method comprising normalizing or adjusting theamplitudes of electrogram signals acquired from electrodes, especiallyconfigured to be located inside the patient's heart, assigning positionsor identifiers for each of the electrodes to corresponding individualelectrogram signals, providing or generating a two-dimensional (2D)spatial map of the electrode positions, for each or selected discretetimes over which the electrogram signals are being processed, processingthe amplitude-adjusted electrogram signals to generate a plurality ofthree-dimensional electrogram surfaces corresponding at least partiallyto the 2D map, one surface being generated for each such time, andprocessing the plurality of three-dimensional electrogram surfacesthrough time to generate a velocity vector map corresponding at leastpartially to the 2D map, the velocity vector map being configured toreveal the location of the source of the at least one cardiac rhythmdisorder.

Electrogram signals and processed data may be delivered or communicatedto the system for performing the method, e.g., via a data carrier, afterthey have been acquired by the electrodes and stored for laterprocessing by the system and method according to this invention. Furtheradvantageous embodiments of the system and method are disclosed hereinor will become apparent to those skilled in the art after having readand understood the claims, specification and drawings hereof.

BRIEF DESCRIPTION OF THE DRAWINGS

Different aspects of the various embodiments will become apparent fromthe following specification, drawings and claims in which:

FIG. 1(a) shows one embodiment of a combined cardiacelectrophysiological mapping (EP), pacing and ablation system 100;

FIG. 1(b) shows one embodiment of a computer system 300;

FIG. 2 shows an illustrative view of one embodiment of a distal portionof catheter 110 inside a patient's left atrium 14;

FIG. 3 shows an illustrative embodiment of a mapping electrode assembly120 of catheter 110 of FIG. 2;

FIG. 4 shows one embodiment of an algorithm or method 200 of detecting alocation of a source of at least one cardiac rhythm disorder in apatient's heart;

FIG. 5(a) shows a simple rotor model;

FIG. 5(b) shows sensed artifacts in electrogram signals;

FIG. 5(c) shows the artifacts of FIG. 5(b) superimposed on simulated ECGsignals;

FIG. 5(d) shows a box plot corresponding to an 8×8 array of 64 electrodesignals;

FIG. 5(e) shows the data of FIG. 5(d) after they have been subjected toan electrode signal normalization, adjustment and filtering process;

FIG. 5(f) shows a surface generated from the data shown in FIG. 5(e);

FIG. 5(g) shows wavefront velocity vectors;

FIGS. 6(a) through 6(c) show details regarding one embodiment of methodor algorithm 200 shown in FIG. 4;

FIGS. 7(a) through 7(j) show the results of processing simulated atrialcardiac rhythm disorder data in accordance with one embodiment of methodor algorithm 200;

FIGS. 8(a) and 8(b) show velocity vector maps generated from actualpatient data using different time windows and method or algorithm 200;

FIG. 9 shows another vector velocity map generated from actual patientdata using method or algorithm 200, and

FIGS. 10(a) through 10(d) show further results obtained using actualpatient data.

The drawings are not necessarily to scale. Like numbers refer to likeparts or steps throughout the drawings.

DETAILED DESCRIPTIONS OF SOME EMBODIMENTS

Described herein are various embodiments of systems, devices, componentsand methods for aiding in the diagnosis and treatment cardiac rhythmdisorders in a patient's heart using electrophysiological mappingtechniques, as well as imaging, navigation, cardiac ablation and othertypes of medical systems, devices, components, and methods. Variousembodiments described and disclosed herein also relate to systems,devices, components and methods for discovering with enhanced precisionthe location(s) of the source(s) of different types of cardiac rhythmdisorders and irregularities. Such cardiac rhythm disorders andirregularities, include, but are not limited to, arrhythmias, atrialfibrillation (AF or A-fib), atrial tachycardia, atrial flutter,paroxysmal fibrillation, paroxysmal flutter, persistent fibrillation,ventricular fibrillation (V-fib), ventricular tachycardia, atrialtachycardia (A-tach), ventricular tachycardia (V-tach), supraventriculartachycardia (SVT), paroxysmal supraventricular tachycardia (PSVT),Wolff-Parkinson-White syndrome, bradycardia, sinus bradycardia, ectopicatrial bradycardia, junctional bradycardia, heart blocks,atrioventricular block, idioventricular rhythm, areas of fibrosis,breakthrough points, focus points, re-entry points, premature atrialcontractions (PACs), premature ventricular contractions (PVCs), andother types of cardiac rhythm disorders and irregularities.

Systems and methods configured to detect in a patient's heart a locationof a source of at least one cardiac rhythm disorder are disclosedherein. In the following description, for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of example embodiments or aspects. It will be evident,however, to one skilled in the art that an example embodiment may bepracticed without necessarily using all of the disclosed specificdetails.

Referring now to FIG. 1(a), there is illustrated one embodiment of acombined cardiac electrophysiological (EP) mapping, pacing and ablationsystem 100. Note that in some embodiments system 100 may not includeablation module 150 and/or pacing module 160. Among other things, theembodiment of system 100 shown in FIG. 1(a) is configured to detect andreconstruct cardiac activation information acquired from a patient'sheart relating to cardiac rhythm disorders and/or irregularities, and isfurther configured to detect and discover the location of the source ofsuch cardiac rhythm disorders and/or irregularities with enhancedprecision relative to prior art techniques. In some embodiments, system100 is further configured to treat the location of the source of thecardiac rhythm disorder or irregularity, for example by ablating thepatient's heart at the detected location.

The embodiment of system 100 shown in FIG. 1(a) comprises five mainfunctional units: electrophysiological mapping (EP mapping) unit 140(which is also referred to herein as data acquisition device 140),ablation module 150, pacing module 160, imaging and/or navigation system70, and computer or computing device 300. In one embodiment, at leastone computer or computing device or system 300 is employed to controlthe operation of one or more of systems, modules and devices 140, 150,160, 170 and 70. Alternatively, the respective operations of systems,modules or devices 140, 150, 160, 170 and 70 may be controlledseparately by each of such systems, modules and devices, or by somecombination of such systems, modules and devices.

Computer or computing device 300 may be configured to receive operatorinputs from an input device 320 such as a keyboard, mouse and/or controlpanel. Outputs from computer 300 may be displayed on display or monitor324 or other output devices (not shown in FIG. 1(a)). Computer 300 mayalso be operably connected to a remote computer or analytic database orserver 328. At least each of components, devices, modules and systems60, 110, 140, 146, 148, 150, 170, 300, 324 and 328 may be operablyconnected to other components or devices by wireless (e.g., Bluetooth)or wired means. Data may be transferred between components, devices,modules or systems through hardwiring, by wireless means, or by usingportable memory devices such as USB memory sticks.

During electrophysiological (EP) mapping procedures, multi-electrodecatheter 110 is typically introduced percutaneously into the patient'sheart 10. Catheter 110 is passed through a blood vessel (not shown),such as a femoral vein or the aorta, and thence into an endocardial sitesuch as the atrium or ventricle of the heart 10.

It is contemplated that other catheters, including other types ofmapping or EP catheters, lasso catheters, pulmonary vein isolation (PVI)ablation catheters (which can operate in conjunction with sensing lassocatheters), ablation catheters, navigation catheters, and other types ofEP mapping catheters such as EP monitoring catheters and spiralcatheters may also be introduced into the heart, and that additionalsurface electrodes may be attached to the skin of the patient to recordelectrocardiograms (ECGs).

When system 100 is operating in an EP mapping mode, multi-electrodecatheter 110 functions as a detector of intra-electrocardiac signals,while optional surface electrodes may serve as detectors of surfaceECGs. In one embodiment, the analog signals obtained from theintracardiac and/or surface electrodes are routed by multiplexer 146 todata acquisition device 140, which comprises an amplifier 142 and an A/Dconverter (ADC) 144. The amplified or conditioned electrogram signalsmay be displayed by electrocardiogram (ECG) monitor 148. The analogsignals are also digitized via ADC 144 and input into computer 300 fordata processing, analysis and graphical display.

In one embodiment, catheter 110 is configured to detect cardiacactivation information in the patient's heart 10, and to transmit thedetected cardiac activation information to data acquisition device 140,either via a wireless or wired connection. In one embodiment that is notintended to be limiting with respect to the number, arrangement,configuration, or types of electrodes, catheter 110 includes a pluralityof 64 electrodes 82, probes and/or sensors A1 through H8 arranged in an8×8 grid that are included in electrode mapping assembly 120, which isconfigured for insertion into the patient's heart through the patient'sblood vessels and/or veins. Other numbers, arrangements, configurationsand types of electrodes 82 in catheter 110 are, however, alsocontemplated. In most of the various embodiments, at least someelectrodes, probes and/or sensors included in catheter 110 areconfigured to detect cardiac activation or electrical signals, and togenerate electrocardiograms or electrogram signals, which are thenrelayed by electrical conductors from or near the distal end 112 ofcatheter 110 to proximal end 116 of catheter 110 to data acquisitiondevice 140.

Note that in some embodiments of system 100, multiplexer 146 is notemployed for various reasons, such as sufficient electrical conductorsbeing provided in catheter 110 for all electrode channels, or otherhardware design considerations. In other embodiments, multiplexer 146 isincorporated into catheter 110 or into data acquisition device 140.

In one embodiment, a medical practitioner or health care professionalemploys catheter 110 as a roving catheter to locate the site of thelocation of the source of a cardiac rhythm disorder or irregularity inthe endocardium quickly and accurately, without the need for open-chestand open-heart surgery. In one embodiment, this is accomplished by usingmulti-electrode catheter 110 in combination with real-time ornear-real-time data processing and interactive display by computer 300,and optionally in combination with imaging and/or navigation systern 70.In one embodiment, multi-electrode catheter 110 deploys at least atwo-dimensional array of electrodes against a site of the endocardium ata location that is to be mapped, such as through the use of a BiosenseWebster® PENTARAY® EP mapping catheter. The intracardiac or electrogramsignals detected by the catheter's electrodes provide data sampling ofthe electrical activity in the local site spanned by the array ofelectrodes.

In one embodiment, the electrogram signal data are processed by computer300 to produce a display showing the locations(s) of the source(s) ofcardiac rhythm disorders and/or irregularities in the patient's heart 10in real-time or near-real-time, further details of which are providedbelow. That is, at and between the sampled locations of the patient'sendocardium, computer 300 may be configured to compute and display inreal-time or near-real-time an estimated, detected and/or determinedlocation(s) of the site(s), source(s) or origin)s) of the cardiac rhythmdisorder(s) and/or irregularity(s) within the patient's heart 10. Thispermits a medical practitioner to move interactively and quickly theelectrodes 82 of catheter 110 towards the location of the source of thecardiac rhythm disorder or irregularity.

In some embodiments of system 100, one or more electrodes, sensors orprobes detect cardiac activation from the surface of the patient's bodyas surface ECGs, or remotely without contacting the patient's body(e.g., using magnetocardiograms). In another example, some electrodes,sensors or probes may derive cardiac activation information fromechocardiograms. In various embodiments of system 100, external orsurface electrodes, sensors and/or probes can be used separately or indifferent combinations, and further may also be used in combination withintracardiac electrodes, sensors and/or probes inserted within thepatient's heart 10. Many different permutations and combinations of thevarious components of system 100 are contemplated having, for example,reduced, additional or different numbers of electrical sensing and othertypes of electrodes, sensors and/or transducers.

Continuing to refer to FIG. 1(a), EP mapping system or data acquisitiondevice 140 is configured to condition the analog electrogram signalsdelivered by catheter 110 from electrodes 82 A1 through H8 in amplifier142. Conditioning of the analog electrogram signals received byamplifier 142 may include, but is not limited to, low-pass filtering,high-pass filtering, bandpass filtering, and notch filtering. Theconditioned analog signals are then digitized in analog-to-digitalconverter (ADC) 144. ADC 144 may further include a digital signalprocessor (DSP) or other type of processor which is configure to furtherprocess the digitized electrogram signals (e.g., low-pass filter,high-pass filter, bandpass filter, notch filter, automatic gain control,amplitude adjustment or normalization, artifact removal, etc.) beforethey are transferred to computer or computing device 300 for furtherprocessing and analysis.

As discussed above, in some embodiments, multiplexer 146 is separatefrom catheter 110 and data acquisition device 140, and in otherembodiments multiplexer 146 is combined in catheter 110 or dataacquisition device 140.

In some embodiments, the rate at which individual electrogram and/or ECGsignals are sampled and acquired by system 100 can range between about0.25 milliseconds and about 8 milliseconds, and may be about 0.5milliseconds, about 1 millisecond, about 2 milliseconds or about 4milliseconds. Other sample rates are also contemplated. While in someembodiments system 100 is configured to provide unipolar signals, inother embodiments system 100 is configured to provide bipolar signals.

In one embodiment, system 100 can include a BARD® LABSYSTEM™ PRO EPRecording System, which is a computer and software driven dataacquisition and analysis tool designed to facilitate the gathering,display, analysis, pacing, mapping, and storage of intracardiac EP data.Also in one embodiment, data acquisition device 140 can include a BARD®CLEARSIGN™ amplifier, which is configured to amplify and conditionelectrocardiographic signals of biologic origin and pressure transducerinput, and transmit such information to a host computer (e.g., computer300 or another computer).

As shown in FIG. 1(a), and as described above, in some embodimentssystem 100 includes ablation module 150, which may be configured todeliver RF ablation energy through catheter 110 and correspondingablation electrodes disposed near distal end 112 thereof, and/or todeliver RF ablation energy through a different catheter (not shown inFIG. 1(a)). Suitable ablation systems and devices include, but are notlimited to, cryogenic ablation devices and/or systems, radiofrequencyablation devices and/or systems, ultrasound ablation devices and/orsystems, high-intensity focused ultrasound (HIFU) devices and/orsystems, chemical ablation devices and/or systems, and laser ablationdevices and/or systems.

When system 100 is operating in an optional ablation mode,multi-electrode catheter 110 fitted with ablation electrodes, or aseparate ablation catheter, is energized by ablation module 150 underthe control of computer 300, control interface 170, and/or anothercontrol device or module. For example, an operator may issue a commandto ablation module 150 through input device 320 to computer 300. In oneembodiment, computer 300 or another device controls ablation module 150through control interface 170. Control of ablation module 150 caninitiate the delivery of a programmed series of electrical energy pulsesto the endocardium via catheter 110 (or a separate ablation catheter,not shown in FIG. 1(a)). One embodiment of an ablation method and deviceis disclosed in U.S. Pat. No. 5,383,917 to Desai et al., the entirety ofwhich is hereby incorporated by reference herein.

In an alternative embodiment, ablation module 150 is not controlled bycomputer 300, and is operated manually directly under operator control.Similarly, pacing module 160 may also be operated manually directlyunder operator control. The connections of the various components ofsystem 100 to catheter 110, to auxiliary catheters, or to surfaceelectrodes may also be switched manually or using multiplexer 146 oranother device or module.

When system 100 is operating in an optional pacing mode, multi-electrodecatheter 110 is energized by pacing module 160 operating under thecontrol of computer 300 or another control device or module. Forexample, an operator may issue a command through input device 320 suchthat computer 300 controls pacing module 160 through control interface170, and multiplexer 146 initiates the delivery of a programmed seriesof electrical simulating pulses to the endocardium via the catheter 110or another auxiliary catheter (not shown in FIG. 1(a)). One embodimentof a pacing module is disclosed in M. E. Josephson et al., in“VENTRICULAR ENDOCARDIAL PACING II, The Role of Pace Mapping to LocalizeOrigin of Ventricular Tachycardia,” The American Journal of Cardiology,vol. 50, November 1982.

Computing device or computer 300 is appropriately configured andprogrammed to receive or access the electrogram signals provided by dataacquisition device 140. Computer 300 is further configured to analyze orprocess such electrogram signals in accordance with the methods,functions and logic disclosed and described herein so as to permitreconstruction of cardiac activation information from the electrogramsignals. This, in turn, makes it possible to locate with at least somereasonable degree of precision the location of the source of a heartrhythm disorder or irregularity. Once such a location has beendiscovered, the source may be eliminated or treated by means thatinclude, but are not limited to, cardiac ablation.

In one embodiment, and as shown in FIG. 1(a), system 100 also comprisesa physical imaging and/or navigation system 70. Physical imaging and/ornavigation device 60 included in system 70 may be, by way of example, a2- or 3-axis fluoroscope system, an ultrasonic system, a magneticresonance imaging (MRI) system, a computed tomography (CT) imagingsystem, and/or an electrical impedance tomography EIT) system. Operationof system 70 be controlled by computer 300 via control interface 170, orby other control means incorporated into or operably connected toimaging or navigation system 70. In one embodiment, computer 300 oranother computer triggers physical imaging or navigation system 60 totake “snap-shot” pictures of the heart 10 of a patient (body not shown).A picture image is detected by a detector 62 along each axis of imaging,and can include a silhouette of the heart as well as a display of theinserted catheter 110 and its electrodes 82 A1-H8 (more about which issaid below), which is displayed on imaging or navigation display 64.Digitized image or navigation data may be provided to computer 300 forprocessing and integration into computer graphics that are subsequentlydisplayed on monitor or display 64 and/or 324.

In one embodiment, system 100 further comprises or operates inconjunction with catheter or electrode position transmitting and/orreceiving coils or antennas located at or near the distal end of an EPmapping catheter 110, or that of an ablation or navigation catheter 110,which are configured to transmit electromagnetic signals for intra-bodynavigational and positional purposes.

In one embodiment, imaging or navigation system 70 is used to helpidentify and determine the precise two- or three-dimensional positionsof the various electrodes included in catheter 110 within patient'sheart 10, and is configured to provide electrode position data tocomputer 300. Electrodes, position markers, and/or radio-opaque markerscan be located on various portions of catheter 110, mapping electrodeassembly 120 and/or distal end 112, or can be configured to act asfiducial markers for imaging or navigation system 70.

Medical navigation systems suitable for use in the various embodimentsdescribed and disclosed herein include, but are not limited to,image-based navigation systems, model-based navigation systems, opticalnavigation systems, electromagnetic navigation systems (e.g., BIOSENSE®WEBSTER® CARTO® system), and impedance-based navigation systems (e.g.,the St. Jude® ENSITE™ VELOCITY™ cardiac mapping system), and systemsthat combine attributes from different types of imaging AND navigationsystems and devices to provide navigation within the human body (e.g.,the MEDTRONIC® STEALTHSTATION® system).

In view of the structural and functional descriptions provided herein,those skilled in the art will appreciate that portions of the describeddevices and methods may be configured as methods, data processingsystems, or computer algorithms. Accordingly, these portions of thedevices and methods described herein may take the form of a hardwareembodiment, a software embodiment, or an embodiment combining softwareand hardware, such as shown and described with respect to computersystem 300 illustrated in FIG. 1(b). Furthermore, portions of thedevices and methods described herein may be a computer algorithm storedin a computer-usable storage medium having computer readable programcode on the medium. Any suitable computer-readable medium may beutilized including, but not limited to, static and dynamic storagedevices, hard disks, optical storage devices, and magnetic storagedevices.

Certain embodiments of portions of the devices and methods describedherein are also described with reference to block diagrams of methods,systems, and computer algorithm products. It will be understood thatsuch block diagrams, and combinations of blocks diagrams in the Figures,can be implemented using computer-executable instructions. Thesecomputer-executable instructions may be provided to one or moreprocessors of a general purpose computer, a special purpose computer, orany other suitable programmable data processing apparatus (or acombination of devices and circuits) to produce a machine, such that theinstructions, which executed via the processor(s), implement thefunctions specified in the block or blocks of the block diagrams.

These computer-executable instructions may also be stored in acomputer-readable memory that can direct computer 300 or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory result in an article of manufacture including instructions whichimplement the function specified in an individual block, plurality ofblocks, or block diagram. The computer program instructions may also beloaded onto computer 300 or other programmable data processing apparatusto cause a series of operational steps to be performed on the computeror other programmable apparatus to produce a computer implementedprocess such that the instructions which execute on computer 300 orother programmable apparatus provide steps for implementing thefunctions specified in an individual block, plurality of blocks, orblock diagram.

In this regard, FIG. 1(b) illustrates only one example of a computersystem 300 (which, by way of example, can include multiple computers orcomputer workstations) that can be employed to execute one or moreembodiments of the devices and methods described and disclosed herein,such as devices and methods configured to acquire and process sensor orelectrode data, to process image data, and/or transform sensor orelectrode data and image data associated with the analysis of cardiacelectrical activity and the carrying out of the combinedelectrophysiological mapping and analysis of the patient's heart 10 andablation therapy delivered thereto.

Computer system 300 can be implemented on one or more general purposecomputer systems or networked computer systems, embedded computersystems, routers, switches, server devices, client devices, variousintermediate devices/nodes or standalone computer systems. Additionally,computer system 300 or portions thereof may be implemented on variousmobile devices such as, for example, a personal digital assistant (PDA),a laptop computer and the like, provided the mobile device includessufficient processing capabilities to perform the requiredfunctionality.

In one embodiment, computer system 300 includes processing unit 301(which may comprise a CPU, controller, microcontroller, processor,microprocessor or any other suitable processing device), system memory302, and system bus 303 that operably connects various systemcomponents, including the system memory, to processing unit 301.Multiple processors and other multi-processor architectures also can beused to form processing unit 301. System bus 303 can comprise any ofseveral types of suitable bus architectures, including a memory bus ormemory controller, a peripheral bus, or a local bus. System memory 302can include read only memory (ROM) 304 and random access memory (RAM)305. A basic input/output system (BIOS) 306 can be stored in ROM 304 andcontain basic routines configured to transfer information and/or dataamong the various elements within computer system 300.

Computer system 300 can include a hard disk drive 303, a magnetic diskdrive 308 (e.g., to read from or write to removable disk 309), or anoptical disk drive 310 (e.g., for reading CDROM disk 311 or to read fromor write to other optical media). Hard disk drive 303, magnetic diskdrive 308, and optical disk drive 310 are connected to system bus 303 bya hard disk drive interface 312, a magnetic disk drive interface 313,and an optical drive interface 314, respectively. The drives and theirassociated computer-readable media are configured to provide nonvolatilestorage of data, data structures, and computer-executable instructionsfor computer system 300. Although the description of computer-readablemedia above refers to a hard disk, a removable magnetic disk and a CD,other types of media that are readable by a computer, such as magneticcassettes, flash memory cards, digital video disks and the like, in avariety of forms, may also be used in the operating environment;further, any such media may contain computer-executable instructions forimplementing one or more parts of the devices and methods described anddisclosed herein.

A number of program modules may be stored in drives and RAM 303,including operating system 315, one or more application programs 316,other program modules 313, and program data 318. The applicationprograms and program data can include functions and methods programmedto acquire, process and display electrical data from one or moresensors, such as shown and described herein. The application programsand program data can include functions and methods programmed andconfigured to process data acquired from a patient, e.g. for assessingheart function, such as shown and described herein with respect to FIGS.1-10(f).

A health care provider or other user may enter commands and informationinto computer system 300 through one or more input devices 320, such asa pointing device (e.g., a mouse, a touch screen, etc.), a keyboard, amicrophone, a joystick, a game pad, a scanner, and the like. Forexample, the user can employ input device 320 to edit or modify the databeing input into a data processing algorithm (e.g., only datacorresponding to certain time intervals). These and other input devices320 may be connected to processing unit 301 through a correspondinginput device interface or port 322 that is operably coupled to thesystem bus, but may be connected by other interfaces or ports, such as aparallel port, a serial port, or a universal serial bus (USB). One ormore output devices 324 (e.g., display, a monitor, a printer, aprojector, or other type of display device) may also be operablyconnected to system bus 303 via interface 326, such as through a videoadapter.

Computer system 300 may operate in a networked environment employinglogical connections to one or more remote computers, such as remotecomputer 328. Remote computer 328 may be a workstation, a computersystem, a router, or a network node, and may include connections to manyor all the elements described relative to computer system 300. Thelogical connections, schematically indicated at 330, can include a localarea network (LAN) and/or a wide area network (WAN).

When used in a LAN networking environment, computer system 300 can beconnected to a local network through a network interface or adapter 332.When used in a WAN networking environment, computer system 300 mayinclude a modem, or may be connected to a communications server on theLAN. The modem, which may be internal or external, can be connected tosystem bus 303 via an appropriate port interface. In a networkedenvironment, application programs 316 or program data 318 depictedrelative to computer system 300, or portions thereof, may be stored in aremote memory storage device 340.

Referring now to FIG. 2, there is shown an illustrative view of oneembodiment of a distal portion of catheter 110 inside a patient's leftatrium 14. As shown in FIG. 2, heart 10 includes right atrium 12, leftatrium 14, right ventricle 18, and left ventricle 20. Mapping electrodeassembly 120 is shown in an expanded or open state inside left atrium 13after it has been inserted through the patient's inferior vena cava andforamen ovalen (“IVC” and “FO” in FIG. 2), and is configured to obtainelectrogram signals from left atrium 12 via an 8×8 array of electrodes82 A1 through H8. Mapping electrode assembly and catheter 110 may alsobe positioned with the patient's right atrium 12, left ventricle 18 andright ventricle 20.

FIG. 3 shows an illustrative embodiment of a mapping electrode assembly120, which in FIG. 3 forms a distal portion of a Boston Scientific®CONSTELLATION® full contact mapping catheter. The CONSTELLATION EPcatheter permits full-contact mapping of a patient's heart chamber, andmay also be employed to facilitate the assessment of entrainment,conduction velocity studies, and refractory period in a patient's heart10. Mapping electrode assembly 120 shown in FIG. 3 permits thesimultaneous acquisition of longitudinal and circumferential signals formore accurate 3-D mapping, and features a flexible basket design thatconforms to atrial anatomy and aids aid in accurate placement.Sixty-four electrodes 82 A1 through H8 can provide comprehensive,real-time 3-D information over a single heartbeat.

FIG. 4 shows one embodiment of a method 200 of detecting a location of asource of at least one cardiac rhythm disorder in a patient's heart. Atstep 210, the amplitudes of electrogram signals acquired from electrodes82 located inside a patient's heart, e.g., electrodes 82 included in amapping electrode assembly 120, are normalized and/or adjusted. At step230, positions A1 through H8 corresponding to each of the electrodes 82of mapping electrode assembly 120 are assigned to the individualelectrogram signals that have been acquired. At step 230, atwo-dimensional (2D) spatial map of electrode positions A1 through H8 isgenerated or provided. In some embodiments, a three-dimensional (3D)spatial map of electrode positions A1 through H8 is generated orprovided. (As discussed above, fewer or more than 64 electrodes 82 maybe used to measure electrogram signals and/or surface ECGs, andelectrode arrays other than 8×8 or rectangular grids are contemplated inthe various embodiments.)

For discrete or selected times over which the electrogram signals arebeing analyzed and processed, at step 240 the amplitude-adjustedelectrogram signals are processed across the 2D (or 3D) map to generatea plurality of three-dimensional electrogram surfaces (which accordingto one embodiment may be smoothed electrogram surfaces), one surfacebeing generated for each such discrete time. At step 250, the pluralityof three-dimensional electrogram surfaces that have been generatedacross the 2D (or 3D) map through time are processed to generate avelocity vector map. The velocity vector map is configured to reveal thelocation of the source of the at least one cardiac rhythm disorder. In asubsequent optional step (not shown in FIG. 4), method 200 furthercomprises ablating patient's heart 10 at the location of the source ofthe cardiac rhythm disorder indicated by the velocity vector map.

Algorithm 200 outlined in FIG. 4 presents one embodiment of a method ofprocessing electrogram signals provided by one or more mapping cathetersso as to transform time domain waveform information into space domaininformation, and then calculate velocity vector maps that correspond tonormalized space potential profile movements for each point in space.For reasons that are explained below, algorithm 200 has the advantagesthat it is robust against artifacts and provides a virtual resolutionthat is higher than the actual electrode density employed to acquire theEP mapping data through the use of a fitting algorithm that determinesthe most likely mean spatial velocity map derived from hundreds ofindividual samples of amplitude patterns recorded by the mappingelectrodes.

As described above, in step 210 of FIG. 4 the amplitudes of electrogramsignals acquired from electrodes located inside the patient's heart arenormalized or otherwise adjusted. In step 240, the amplitude-adjustedelectrogram signals are processed across a 2D or 3D map to generate aplurality of three-dimensional electrogram surfaces, one surface beinggenerated for each such discrete time. In one embodiment, the resultingindividual time-slice surfaces can be strung together sequentially toprovide a time-varying depiction of electrical activation occurring overthe portion of the patient's heart that has been monitored. According toembodiments that have been discovered to be particularly efficacious inthe field of intracardiac EP monitoring and data processing andanalysis, at least portions of the electrogram surfaces are found tocorrespond to estimated wave shapes, and are generated using Green'sfunction, which in some embodiments, and by way of non-limiting example,may be combined with two- or three-dimensional bi-harmonic splineinterpolation functions to generate such surfaces.

In one embodiment, electrogram signal data acquired from the patient'sheart 10 are not equidistantly sampled. For example, in one suchembodiment, electrogram signal data acquired from the patient's heart 10are not equidistantly sampled by mapping electrode assembly 120, andinstead are assigned their respective chessboard locations A1 through H8as approximations of electrode locations in a cylindrical 2D projectionof a grid representative of the interior surface of the patient's heartthat is being mapped. In many applications, it has been discovered thatsuch approximations of electrode locations yield perfectly useable andaccurate results when steps 230 through 250 are carried out after steps210 and 230.

In another embodiment, when superimposing the acquired electrogramsignal data onto a 2D or 3D map or grid in step 230, the electrogramsignal data may be associated with their actual or more accuratelyestimated positions in the 2D projection of the grid using positionaldata provided by, for example, imaging or navigation system 70.Resampling of electrogram signals on the grid may also be carried out.Gridding may also be carried out such as by convolution-type filtering,Kriging, and using splines. Most gridding techniques operate on anequidistant grid and solve the equations governing the gridding processwith either finite difference or finite element implementations.

One approach that has been discovered to work particularly well withelectrogram signal data is to determine the Green's function associatedwith each electrogram value assigned to a given chessboard location, andthen construct the solution as a sum of contributions from each datapoint, weighted by the Green's function evaluated for each point ofseparation. Biharmonic spline interpolation, which can be used inconjunction with Green's function, has also been discovered to workespecially well in the context of processing and analyzing electrogramsignal data. In some embodiments, undesirable oscillations between datapoints are removed by interpolation with splines in tension based onGreen's function. A Green's function technique for interpolation andsurface fitting and generation of electrogram signal data has been foundto be superior to conventional finite-difference methods because, amongother things, the model can be evaluated at arbitrary x,y locationsrather than only on a rectangular grid. This is a very importantadvantage of using Green's function in step 240, because preciseevenly-spaced-apart grid locations, resampling of electrogram signals,and finite-difference gridding calculations are not required to generateaccurate representations of electrogram surfaces in step.

In one embodiment, Green's function G(x; x′) is employed in step 240 fora chosen spline and geometry to interpolate data at regular or arbitraryoutput locations. Mathematically, the solution is w(x)=sum {c(i) G(x′;x(i))}, for i=1, n, and a number of data points {x(i), w(i)}. Once the ncoefficients c(i) have been calculated, the sum may be evaluated at anyoutput point x. A selection is made between minimum curvature,regularized, or continuous curvature splines in tension for either 1-D,2-D, or 3-D Cartesian coordinates or spherical surface coordinates.After removing a linear or planar trend (i.e., in Cartesian geometries)or mean values (i.e., spherical surfaces) and normalizing residuals, aleast-squares matrix solution for spline coefficients c(i) may bedetermined by solving the n by n linear system w(j)=sum-over-i {c(i)G(x(j); x(i))}, for j=1, n; this solution yields an exact interpolationof the supplied data points. For further details regarding thealgorithms and mathematics underlying Green's function, see: (1) “MovingSurface Spline Interpolation Based on Green's Function,” Xingsheng Dengand Zhong-an Tang, Math. Geosci (2011), 43:663-680 (“the Deng paper”),and (2) “Interpolation with Splines in Tension: A Green's FunctionApproach,” Paul Wessel and David Bercovici, Mathematical Geology, 77-93,Vol. 30, No. 1, 1998 (“the Wessel paper”). The respective entireties ofthe Deng and Wessel papers are hereby incorporated by reference herein.

Still further details regarding the use of Green's function ininterpolating and generating surfaces may be found in: Interpolation byregularized spline with tension: I. Theory and implementation, Mitasova,H., and L. Mitas, 1993, Math. Geol., 25, 641-655; Parker, R. L., 1994,Geophysical Inverse Theory, 386 pp., Princeton Univ. Press, Princeton,N.J.; Sandwell, D. T., 1987, Biharmonic spline interpolation of Geos-3and Seasat altimeter data, Geophys. Res. Lett., 14, 139-142; Wessel, P.,and J. M. Becker, 2008, Interpolation using a generalized Green'sfunction for a spherical surface spline in tension, Geophys. J. Int,174, 21-28, and Wessel, P., 2009, A general-purpose Green's functioninterpolator, Computers & Geosciences, 35, 1247-1254. Moving SurfaceSpline Interpolation Based on Green's Function, Xingsheng Deng, Zhong-anTang, Mathematical Geosciences, August 2011, Volume 43, Issue 6, pp663-680.

Note, however, that a number of different surface smoothing, surfacefitting, surface estimation and/or surface/data interpolation processingtechniques may be employed in step 240 of FIG. 4, which are not limitedto Green's function, and which include, but are not limited to, inversedistance weighted methods of interpolation, triangulation with linearinterpolation, bilinear surface interpolation methods, bivariate surfaceinterpolation methods, cubic convolution interpolation methods, Kriginginterpolation methods, Natural Neighbor or “area-stealing” interpolationmethods, spline interpolation techniques (including bi-harmonic splinefitting techniques and “spline with barriers” surface interpolationmethods), global polynomial interpolation methods, moving least squaresinterpolation methods, polynomial least square fitting interpolationmethods, simple weighted-average operator interpolation methods,multiquadric bi-harmonic function interpolation methods, and artificialneural network interpolation methods. See, for example: “A briefdescription of natural neighbor interpolation (Chapter 2),” in V.Barnett. Interpreting Multivariate Data. Chichester: John Wiley. pp.21-36.), and “Surfaces generated by Moving Least Squares Methods,” P.Lancaster et al., Mathematics of Computation, Vol. 37, No. 155 (July,1981), 141-158).

As described above, in step 250 of FIG. 4, the plurality ofthree-dimensional electrogram surfaces may be processed across the 2D or3D map through time to generate a velocity vector map, the velocityvector map being configured to reveal the location of the source of theat least one cardiac rhythm disorder. According to embodiments that havebeen discovered to be particularly efficacious in the field ofintracardiac EP monitoring and subsequent data processing and analysis,at least portions of the velocity vector map are generated using one ormore optical flow analysis and estimation techniques and methods. Suchoptical flow analysis techniques may include one or more ofHorn-Schunck, Buxton-Buston, Black-Jepson, phase correlation,block-based, discrete optimization, Lucas-Kanade, and differentialmethods of estimating optical flow. From among these various opticalflow estimation and analysis techniques and methods, however, theHorn-Schunck method has so far been discovered to provide superiorresults in the context of processing and analyzing cardiac electrogramsignals, for reasons that are discussed in further detail below.

Two papers describe the Horn-Schunck method particularly well: (1)“SimpleFlow: A NonIterative, Sublinear Optical Flow Algorithm,” MichaelTao et al., Eurographics 2012, Vol. 31 (2012), No. 2 (“the Tao paper”),and (2) “Horn-Schunck Optical Flow with a Multi-Scale Strategy,” EnricMeinhardt-Llopis et al., Image Processing On Line, 3 (2013), pp. 151-172(“the Meinhardt-Llopis paper”). The respective entireties of the Tao andMeinhardt-Llopis papers are hereby incorporated by reference herein.

In “Determining Optical Flow,” by B. K. P. Horn and B. G. Schunck,Artificial Intelligence, Vol. 17, pp. 185-204, 1981,the entirety ofwhich is also hereby incorporated by reference herein, a method forfinding an optical flow pattern is described which assumes that theapparent velocity of a brightness pattern varies smoothly throughoutmost of an image. The Horn-Schunck algorithm assumes smoothness in flowover most or all of an image. Thus, the Horn-Schunck algorithm attemptsto minimize distortions in flow and prefers solutions which exhibitsmoothness. The Horn-Schunck method of estimating optical flow is aglobal method which introduces a global constraint of smoothness tosolve the aperture problem of optical flow.

A description of some aspects of conventional application of theHorn-Schunck method is set forth in U.S. Pat. No. 6,480,615 to Sun etal. entitled “Motion estimation within a sequence of data frames usingoptical flow with adaptive gradients,” the entirety of which is alsohereby incorporated by reference herein. As described by Sun et al., theHorn-Schunck computation is based on the observation that flow velocityhas two components, and that a rate of change of image brightnessrequires only one constraint. Smoothness of flow is introduced as asecond constraint to solve for optical flow. The smoothness constraintpresumes there are no spatial discontinuities. As a result, Horn andSchunck excluded situations where objects in an image occlude or blockone another. This is because at object boundaries of an occlusion in animage, discontinuities in reflectance appear.

In conventional optical flow analysis, image brightness is considered atpixel (x,y) in an image plane at time t to be represented as a functionl(x,y,t). Based on initial assumptions that the intensity structures oflocal time-varying image regions are approximately constant under motionfor at least a short duration, the brightness of a particular point inthe image is constant, so that dl/dt=0. Based on the chain rule ofdifferentiation, an optical flow constraint equation (l) can berepresented as follows:

lx(x,y,t)·u+ly(x,y,t)·v+lt(x,y,t)=0,

wherelx=∂l(x,y,t)/∂x=horizontal spatial gradient of the image intensity;ly=∂l(x,y,t)/∂y=vertical spatial gradient of the image intensity;lt=∂l(x,y,t)/∂t=temporal image gradient of the image intensity;u=dx/dt=horizontal image velocity (or displacement); andv=dy/dt=vertical image velocity (or displacement).

The above optical flow equation is a linear equation having twounknowns, (i.e., u and v). The component of motion in the direction ofthe brightness gradient is known to be lt/(lx 2+ly 2)½. However, onecannot determine the component of movement in the direction of theisobrightness contours at right angles to the brightness gradient. As aconsequence, the optical flow velocity (u,v) cannot be computed locallywithout introducing additional constraints. Horn and Schunck thereforeintroduce a smoothness constraint. They argue that if every point of thebrightness pattern can move independently, then there is little hope ofrecovering the velocities. However, if opaque objects of finite size areundergoing rigid motion or deformation, neighboring points on theobjects should have similar velocities. Correspondingly, the velocityfield of the brightness patterns in the image will vary smoothly almosteverywhere.

Advantages of the Horn-Schunck algorithm include that it yields a highdensity of flow vectors, i.e., the flow information missing in innerparts of homogeneous objects is filled in from the motion boundaries. Onthe negative side, the Horn-Schunck algorithm can be sensitive to noise.

The foregoing discussion regarding how the Horn-Schunck optical flowtechnique typically focuses on conventional applications, where thebrightness or intensity of an object changes over time (which is wherethe term “optical flow” is derived from). Here, the brightness orintensity of an object is not the issue at hand. Instead, the amplitudesof electrogram signals, and how they change shape and propagate in timeand space over a patient's heart, are sought to be determined. Oneunderlying objective of method or algorithm 200 is to produce a vectorvelocity map, which is a representation of electrographical flow (andnot optical flow) within e.g. a patient's heart. Instead of looking fordifferences or changes in optical brightness or intensity, changes inthe velocity, direction and shape of electrical signals changes inelectrographical flow) across a patient's heart are determined. That is,algorithm 200 does not process optical measurement data corresponding tointensity or brightness, but processes electrical measurement datacorresponding to amplitude, potential shape, and/or voltage. One of thereasons why algorithm 200 works so well in detecting the locations ofthe sources of cardiac rhythm disorders and irregularities is that ionchannels in a patient's heart produce action potential voltages that arerelatively constant (except in areas of fibrosis). As described above,the Horn-Schunck method assumes “brightness constancy” as one of its keyconstraints. The normalized/amplitude-adjusted electrogram signalsprovided by step 210 help satisfy this key constraint of theHorn-Schunck method so that this method may be applied successfully instep 250.

In addition, because of the stability imparted to electrographical flowsolutions determined using the Horn-Schunck method, artifacts and noiseare generally low in velocity vector maps generated in step 250. Infact, it is believed that the Horn-Schunck method may generally beapplied with greater success to electrographical flow data than tooptical data because of the unique nature of action potential signals inthe human heart, and the manner in which electrogram signals areprocessed and conditioned before an optical flow analysis is performedon them as described and disclosed herein.

Algorithm 200 described and disclosed herein also does not employspatial derivatives of electrical potentials (as is done by Deno et al.and Kumaraswamy Nanthakumar using “omnipolar” signals) or timederivatives of electrogram signals (as is done in the TOPERA system).Time derivatives of signals are known to increase noise. Algorithm 200has as its key inputs the potentials of electrogram signals (not theirderivatives). As a result, algorithm 200 is notably free from theeffects of spurious noise and artifacts introduced by time-derivativedata processing techniques, including in step 250.

In another embodiment, the velocity vector map of step 250 is generatedusing the Lucas-Kanade optical flow algorithm, which is a differentialmethod for optical flow estimation developed by Bruce D. Lucas and TakeoKanade. It assumes that the flow is essentially constant in a localneighbourhood of a pixel under consideration, and solves the basicoptical flow equations for all the pixels in that neighborhood usingleast squares criteria. By combining information from several nearbypixels, the Lucas-Kanade method can often resolve the inherent ambiguityof the optical flow equation. It is also less sensitive to image noisethan point-wise methods. On the other hand, since it is a purely localmethod, it cannot provide flow information in the interior of uniformregions of the image. See “An Iterative Image Registration Techniquewith an Application to Stereo Vision,” Bruce D. Lucase, Takeo Kanade,Proceedings of Imaging Understanding Workshop, pp. 121-130 (1981), theentirety of which is hereby incorporated by reference herein.

In yet another embodiment, various aspects of the Horn-Schunck andLucas-Kanade algorithms are combined to yield an optical flow algorithmthat exhibits the local methods inherent in Lucas-Kanade techniques andthe global methods inherent in the Horn-Schunck approach and itsextensions. Often local methods are more robust under noise, whileglobal techniques yield dense flow fields. See, for example,“Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic FlowMethods,” Andrés Bruhn, Joachim Weickert, Christoph Schnörr,International Journal of Computer Vision, February 2005, Volume 61,Issue 3, pp 211-231, the entirety of which is hereby incorporated byreference herein.

Various embodiments of algorithm 200 feature several advantages withrespect to prior art systems and methods that generate intracardiacimages and attempt to detect the locations of cardiac rhythm disordersor irregularities. A key underlying assumption of signal processingtechniques that employ Hilbert Transform, Discrete Fourier Transforms(DFTs) or Fast Fourier Transforms (FFTs) is that the signal to betransformed is periodic. As is well known in the field of digital signalprocessing, this underlying basic assumption is frequently incorrect,and can lead to problems such as spectral leakage. Contrariwise, in someembodiments of algorithm 200, an underlying assumption is that theelectrical activity in a patient's heart is based upon ion channelactivation, which is a stochastic and non-periodic process, and sostrictly periodic behaviour is not assumed or required in subsequentdata processing and manipulation steps.

Indeed, none of steps 210, 230, 240, or 250 of method or algorithm 200absolutely requires the use of Hilbert or Fourier transforms to processdata. Instead, in some embodiments each of these steps can be carriedout in the time domain without the need for frequency domain orquadrature conversion. For example, in step 210 the amplitudes of thevarious traces or electrograms can be normalized or adjusted in the timedomain according to a selected standard deviation. In another example,rotors detected by algorithm 200 are not assumed to be singularities ina phase map (as is assumed in techniques based upon frequency domain orHilbert transform signal processing). This key difference also explainswhy the rotational direction of a rotor can be revealed or detectedaccurately by algorithm 200 (and not at all, or very unsatisfactorily,using the frequency domain or Hilbert transforms of other methodsemployed to detect rotors). Note that in some embodiments, however,Hilbert, DFT and/or FFT signal processing components may be or areincluded in the data processing flow of algorithm 200 (e.g., DSPfiltering, deconvolution, etc.).

Referring now to FIG. 5(a), there is shown a simple rotor model. Thismodel was used to generate simulated ECG signals sensed by an 8×8 arrayof virtual electrodes. The simple rotor model shown in FIG. 5(a) is from“Chaste: An Open Source C++ Library for Computational Physiology andBiology, ”Gary R. Mirams, et al. PLOS Computational Biology, Mar. 14,2013, Vol. 9, Issue 3, e1002970, the entirety of which is herebyincorporated by reference herein.

FIG. 5(b) shows artifacts in electrogram signals derived from actualpatient data, where 400 msec. traces were recorded using a 64-electrodebasket catheter located in the left atrium of a patient suffering fromatrial fibrillation. As shown in FIG. 5(b), the sensed artifacts in theelectrogram signals include DC offsets of several millivolts that shiftwith time, a common far-field ventricular depolarization superimposed onthe local potentials sensed by individual electrodes, and noise.Moreover, the amplitudes of the various sensed electrogram signals shownin FIG. 5(b) will be seen to vary considerably. These amplitudevariations result at least in part on from varying degrees to whichindividual electrodes touch, or are physically coupled to, the patient'sendocardial surface. Electrogram signals corresponding to electrodes inloose, poor or no contact with a patient's endocardium may be an orderof magnitude smaller than those where electrodes are well coupled to theendocardial surface.

FIG. 5(c) shows the artifacts of FIG. 5(b) superimposed on the simulatedECG signals generated from the rotor model of FIG. 5(a). FIG. 5(d) showsa box plot corresponding to the 8×8 array of 64 electrode signals shownin FIG. 5(a) at a selected common time for all traces. Because of theartifacts from FIG. 5(b) introduced into the electrogram signals of FIG.5(c), the box plot of FIG. 5(d) appears quite irregular and chaotic, andthe original spiral shape of the underlying rotor of FIG. 5(a) is notdiscernable to the eye.

The data shown in FIG. 5(c) were used to perform an analysis inaccordance with algorithm 200, which was carried out in three mainsteps: (1) normalization/adjustment/filtering of electrogram signals(step 210); (2) generating three-dimensional smoothed electrogramsurfaces for discrete times or time slices from thenormalized/adjusted/filtered electrogram signals (step 240) generated inthe first main step 210, and (3) generating a velocity vector map basedon the smoothed electrogram surfaces (step 250) generated in the secondmain step 240.

Described now is one embodiment and illustrative example of the firstmain step of the algorithm 200 (normalization/adjustment/filtering ofelectrogram signals). Referring now to FIG. 5(e), there are shown thedata of FIG. 5(d) after they have been subjected to one embodiment of anelectrode signal normalization, adjustment and filtering process. Afternormalization and filtering, the simple rotor structure shown in FIG.5(a) becomes visible in FIG. 5(e). Uniform electrode signal amplitudeminima and maxima were first calculated and then applied to individualelectrogram signals to generate individual amplitude equalizedelectrogram signals. Unwanted artifacts such as ventriculardepolarization signals were removed from the individual equalizedelectrogram signals by first averaging all electrogram signals togenerate a common electrogram artifact signal, which was then subtractedfrom each of the equalized individual electrogram signals. The resultingequalized artifact-compensated electrogram signals were then high-passfiltered between 5 and 20 Hz to remove DC offsets from the electrogramsignals such that the resulting filtered electrogram signals wereapproximately zeroed around the X (time) axis. These results are shownin FIG. 5(e).

Next, a sliding time window ranging between about 0.1 seconds and aboutto 1 second in length was applied to each filtered electrogram signal togenerate individual amplitude-adjusted electrogram signals. (In someembodiments, the length of the sliding time window corresponds to, or isless than, the slowest repetition frequency expected to be present.) Theresulting sliding-window amplitude-adjusted electrogram signals werethen stored for later use to generate image backgrounds in velocityvector maps, where they could be used to show low amplitude areasindicative of valve defects/artifacts, loose electrode contact, and/orareas of fibrosis in the patient's myocardium. In the sliding-windowamplitude-adjusted electrogram signals, the respective minima and maximaof each position of the sliding time window were used to normalize theamplitude values of all signals between zero and one (or 0 and 255 on an8-bit integer numeric scale). Because the maximum and minimum valuesoccurred at different time points for electrodes placed in differentlocations, this process yielded spatial information regarding actionpotential wave patterns for each sampled time point (more about which issaid below).

Now I describe one embodiment and illustrative example of the secondmain step of the algorithm 200 (generating three-dimensional electrogramsurfaces for discrete times or time slices, or estimation of spatialwave shapes). The second step of algorithm 200 takes the spatialdistributions of all electrodes 82 and their normalized voltage valuesat discrete times (e.g., the data represented by the box plotscorresponding to selected discrete times within the selected time windowover which electrogram signals were acquired and measured), andestimates or generates from such data or box plots corresponding togiven discrete times respective continuous voltage surfaces (or actionpotential waveform estimates) in space. Because the electrode patterndensity is limited, and depending on the method that is used to generatethe estimated voltage surfaces, the estimated surfaces typically deviateto some extent from “true” surfaces. Such deviations are usuallyrelatively small in magnitude, however, since the spatial size of theaction potential wave given by its velocity (e.g., 0.5 to 1 m/sec.)times the action potential duration (e.g., 0.1 to 0.2 sec.) is muchlarger (e.g., 0.05 m) than the electrode spacing (e.g., about 1 mm toabout 10 mm), and thus spatial aliasing generally does not occur. Theelectrode grid provided by catheter 110 thus permits relatively goodestimates of action potential wave shapes or wavefronts in the form ofsmoothed electrogram surfaces to be obtained as they propagate acrossthe myocardium. On the other hand, because of the fast sampling rate(which can, for example, range between about 0.25 milliseconds and about8 milliseconds, and which in some embodiments is nominally about 1millisecond), changes in the spatial shape or expression of the actionpotential wavefront from one sample to the next are typically relativelysmall (e.g., about 1 mm) compared to the electrode distances (which insome embodiments nominally range between about 2 mm and about 7 mm).Thus, algorithm 200 is capable of detecting spatial changes in actionpotential wavefronts or wave shapes using time domain information (i.e.,small amplitude changes between time samples) to estimate changes in thespatial domain (where relatively small shifts in action potentials occurat given electrode measurement locations).

One embodiment of a method for estimating action potential wavefronts orwave shapes employs an 8×8 rectangular electrode grid (e.g.,TOPERA®-like) model, which operates in two principal steps. First, eachelectrode/electrogram signal value at a discrete moment in time definesthe height of its respective box in the “chess field” box plots shown inFIGS. 5(d) and 5(e). Second, a smoothed electrogram surface is generatedfor each box plot (or discrete slice of time) by calculating for eachhorizontal x-y point (typically on a 300×300 grid) an average ofneighboring z-values (or electrical potentials) in the box plot. In 3Dmodels that take assumed or actual electrode positions and spacing intoaccount (using, e.g., information from a navigation or imaging system),smoothed electrogram surfaces are generated using 2D bi-harmonic splineinterpolation techniques and Green's function. Using the foregoingsimple averaging approach, the smoothed electrogram surface of FIG. 5(f)was generated from the data shown in FIG. 5(e). As shown in FIG. 5(f), aspatial wave shape estimate of a rotor appears prominently in theforward center portion of the resulting smoothed surface, which tracksclosely the original spiral wave shown in FIG. 5(a).

Described now is one embodiment and illustrative example of the thirdmain step of algorithm 200 (generating a velocity vector map based onthe electrogram surfaces). The third main step of algorithm 200 uses theaction potential wave shape estimates or electrogram surfaces generatedat discrete times or time splices provided by the second main step tocalculate a velocity vector map. For each sample interval a spatial waveshape or smoothed surface is calculated according to the second mainstep described above. Since the wave shapes differ only by a small deltabetween individual samples, and minimum and maximum values arenormalized, shift vectors can be calculated at a spatial resolution thatis higher than the spatial resolution of the electrodes 82 (e.g., 30×30samples). Since individual shifts between samples may differ accordingto random error, a velocity vector fit can be generated using 40 to 100samples, where an average of observed shift vectors of the actionpotential wave shape care calculated. If the angle of a rotatingwavefront is shifted by a few degrees per sample, the vector arrows 40will exhibit a circular pattern and in fact can resolve circles that aremuch smaller than inter-electrode distances. In one embodiment, thethird main step of the algorithm employs a vector pattern equation thatbest fits the observed movement of the evaluated spatial element orwavefront. In one embodiment that has been discovered to provideexcellent results, and as described above, the velocity vector map iscalculated using the Horn-Schunck optical flow method described above.That is, in one embodiment the Horn-Schunck optical flow method is usedin the third main step of algorithm 200 to estimate the velocity anddirection of wavefronts or wave shapes between sampled times. Velocitiesof 40 to 100 samples are typically averaged to yield the most stableresults.

FIG. 5(g) shows the resulting wavefront velocity vectors (indicated byarrows 40) calculated from a series of 60 averaged time slices ofsmoothed surfaces samples corresponding to the data shown in FIG. 5(f).An active rotor is distinctly visible in the right-hand central portionof FIG. 5(g), where arrows 40 are flowing tightly in a counterclockwisedirection. In FIG. 5(g), action potential wavefronts are seen to bemoving outwardly away from the detected active rotor (as would beexpected in the case of an active rotor)).

Referring now to FIGS. 6(a), 6(b) and 6(c), and with further referenceto FIG. 4, there are shown some of the individual steps corresponding tothe three main steps 230, 240 and 250 carried out according to oneembodiment of algorithm 200 disclosed and described herein.

FIG. 6(a) shows one embodiment of steps 202 through 212 of main step 210of FIG. 4 (“normalize/adjust amplitudes, filter electrogram signals). InFIG. 6(a), step 202 is shown as comprising receiving a data filecorresponding to the EP recording of electrogram signals from a basketor other type of EP recording catheter positioned in a patient's heart10. The time interval over which such electrogram signals are recordedinside the patient's heart 10 may, of course, vary according to, amongother things, the requirements of the examination that is to beperformed, and/or the suspected or known cardiac rhythm disorder fromwhich the patient suffers. Illustrative, but non-limiting, examples ofsuch time intervals range between about a second and one minute or more.Bad or poor fidelity traces or electrograms may be selectively removedor edited at this stage.

At step 204, a high-pass filter is applied to the acquired EP data toremove DC offsets, as well as other undesirable low-frequency noise. Inone embodiment, a 5 Hz high-pass filter is applied, although otherfilters, including band-pass filters, are contemplated, including, butnot limited to, 10 Hz high-pass filters, 5-20 Hz band-pass filters, and5-50 Hz band-pass filters. Notch- and low-pass filtering may also beapplied in step 204. Hanning, trapezoidal and other digital filteringand/or Fast Fourier Transform (FFT) filtering techniques may also beapplied.

At step 206, an average far-field electrogram signal is generated bystacking and averaging all electrogram traces. In the case of atrial EPrecordings, the resulting estimate of a far-field ventriculardepolarization is subtracted from each trace individually, therebyremoving or at least reducing the far-field component therefrom.

At step 208, the amplitudes of individual filtered electrogram signalsare normalized with respect to a given standard deviation occurring overa predetermined time window (e.g., a moving window of 200 samples arounda time value “x”).

At step 212, a complete filtered sample array from the grid or basketcatheter is provided as an output from first main step 210.

Referring now to FIG. 6(b), there is shown one embodiment of the secondmain step 230 of algorithm 200 shown in FIG. 4 (processingamplitude-adjusted electrogram signals across the 2D or 3Drepresentation, map or grid to generate a plurality of three-dimensionalelectrogram surfaces, one surface being generated for each selected orpredetermined discrete time or time slice).

In FIG. 6(b), second main step 240 is shown as including steps 241 and243, which according to one embodiment are performed in parallel ornear-parallel. At step 241, digitally sampled and processed electrogramsignals from step 212 of FIG. 6(a) are provided, and at step 242 anarray of 200×200 empty 3D data points are generated, which correspond tothe 2D or 3D representation, map or grid which is to be generated (orhas already been generated). In one embodiment, such a representation,map or grid is formed by making a cylindrical projection representation,map or grid that corresponds to an approximate estimate or calculatedmap of the region of the patient's myocardial wall where the electrogramsignals were acquired and measured (see step 243) by catheter 110.

Positional data from imaging or navigation system 70 can be provided atthis stage to improve the positional accuracy of the individuallocations within such grid where electrogram signals were acquired. Inone embodiment, for each time slice or sampled time, a Z-value orelectrical potential corresponding to the normalized, adjusted and/orfiltered measured voltage of each individual electrogram is assigned alocation in the representation, map or grid.

At step 244, Green's function, or another suitable surface generatingalgorithm, is used to generate a surface of Z-values for each time sliceor sampled time (more about which is said below). In one embodiment, thesurface corresponding to the Z-values is smoothed.

At step 245, the calculated surface corresponding to each time slice orsampled time is provided as an output, with, for example, a 200×200array of smoothed data points corresponding to the smoothed surfacebeing provided for each time slice or sampled time. Note that in someembodiments the intervals at which time slices are selected, or theindividual time slices themselves, may be predetermined, or may beselected automatically or by the user.

FIG. 6(c) shows step 250 corresponding to one embodiment of the thirdmain step of FIG. 4 (processing the plurality of three-dimensionalelectrogram surfaces generated across a 2D or 3D map through time togenerate a velocity vector map, for example by means of the optical flowanalysis and estimation techniques and methods, such those described anddisclosed elsewhere herein. In FIG. 6(c), third main step 250 is shownas including step 251, which in one embodiment entails sequentiallyaccessing the individual surfaces generated for selected time slicesand/or discrete times in step 240. At steps 252 and 253, adjacent timeslices are analyzed and processed sequentially. In step 254, a spatialgradient corresponding to each point of the representation, map or gridis calculated say over, for example, the last 100 time slices. At step255, a continuous graphical output of calculated flow vectors can beprovided as a real-time or near-real-time output. At step 256, the mostlikely flow vector magnitude (or velocity) and direction for each pointthat minimizes energy is calculated. At step 257, X (or time) isincremented, and the foregoing calculations are repeated and refined,the final output of which is a vector velocity map of the type shown, byway of non-limiting example, in FIGS. 5(g), 7(e), 7(i), 7(j), 7(k),7(l), 8, 9, 10(a), 10(c), and 10(e).

FIGS. 7(a) through 7(j) show the results of processing simulated atrialcardiac rhythm disorder data using the methods and techniques describedand disclosed above, where the concept of analyzing complex rotorstructures was applied to a data set of simulated data. The simulateddata shown in FIG. 7(a) primarily comprised stable active and passiverotors, as described in Carrick et al. in “Prospectively Quantifying thePropensity for Atrial Fibrillation: A Mechanistic Formulation,” R. T.Carrick, P. S. Spector et al.; Mar. 13, 2015, the entirety of which ishereby incorporated by reference herein. From Carrick, et al.'s videocorresponding to the foregoing publication, and referring now to FIG.7(a), stable rotor data were recorded for a frame delineated by theindicated blue square, where there are seven rotors. The recording wasaccomplished using the luminance of the video frame in an 8×8 matrixwith an 8-bit signal depth, thereby to simulate electrogram signal dataacquired using a conventional 64-electrode 8×8 basket catheter. Theoverall video comprised 90 frames. All data shown n FIG. 7(a) were takenfrom frame 60. Signal amplitudes from frame 60 are shown in the chessfield and box plots of FIGS. 7(b) and 7(c), respectively.

In FIG. 7(a), 7 rotors are shown marked with circles within therectangle. In FIG. 7(b), a box plot of 8×8 matrix amplitudes is shownhaving amplitudes corresponding to frame 60. FIG. 7(d) shows theestimated wavefront or smoothed surface corresponding to frame 60. FIG.7(e) shows the vector velocity map generated from the data correspondingto FIG. 7(a) (which was generated on the basis of all 90 frames or timesslices). Reference to FIG. 7(e) shows that seven active rotors (markedwith circles 45) are apparent, as are two passive rotors (marked withstars 46).

Referring now to FIGS. 7(b) and 7(c), it will be seen that the 2D and 3Dbox patterns shown therein provide rough estimates of the spatialwavefronts shown in FIG. 7(a). In FIG. 7(d), however, the original datashown in FIG. 7(a) are reproduced fairly accurately, and also provide agood input to the vector velocity map of FIG. 7(e) (which nicely revealsthe 7 active rotors visible in FIG. 7(a)). The vector arrows 40 in FIG.7(e) not only show the rotational centers of the individual rotors, butalso show that active rotors indicated by circles 45 are driving sourcesof the wave fronts because the calculated vectors of the active rotorsalways point centrifugally away from the rotor centers. In contrast, thetwo stars 46 shown in FIG. 7(e) indicate the locations of passive rotorsor flow turbulences that, while circular in shape, have centripetalvector directions to at least on one side of the rotor centersassociated therewith.

Discrimination between active and passive rotors is critical to makingproper therapeutic decisions regarding the delivery of ablation therapy,which should only target structures underlying the drivers of atrialfibrillation (namely, active rotors only, and not passive rotors).

Next, the effects of typical artifact disturbances on the signals of the64 channels of data shown In FIGS. 7(a) through 7(d) were determined byintroducing simulated variable amplitude DC-offset noise and artifactsinto the electrogram signals. The objective was to test the extent towhich such artifacts and noise might impair or disable the ability ofalgorithm 200 to detect rotors in the data.

FIGS. 7(f) and 7(g) show the same box plot data as FIGS. 7(b) and 7(c),respectively, but with the foregoing-described superimposed andintroduced artifacts. That is, FIGS. 7(f) and 7(g) show the chess fieldand box plots of the disturbed electrogram signals corresponding toframe 60. After filtering and normalization in step 210, the originalrotor structure shown in FIG. 7(a) once again becomes visible in FIG.7(h) following completion of the main second step 240 of the algorithm.

Upon applying smoothed surface calculations and fitting (as shown inFIG. 7(i)), algorithm 200 is seen to detect only five of the sevenactive rotors shown in FIG. 7(a). One additional active rotor, however,was detected at a different location (see FIG. 7(i)).

The largest variation in results was seen at positions where theintroduction of the artifacts and noise reduced relative amplitudevalues by the greatest amount, as indicated by the white areas shown inFIG. 7(j). The white areas shown in FIG. 7(j) were generated by usingthe sliding-window amplitude-adjusted electrogram signal techniquesdescribed above, where electrograms processed using sliding-windowtechniques were used to generate the image background (including thewhite areas) shown in the velocity vector map of FIG. 7(j). The whiteareas in FIG. 7(j) thus correspond to low amplitude areas potentiallyindicative of valve defects or artifacts, loose electrode contact,and/or areas of fibrosis in the patient's myocardium. It is important topoint out that the low-amplitude areas shown in white in the variousvelocity vector maps presented herein are not calculated using Green'sfunction or optical flow data processing techniques. Instead, and asdescribed above, these low-amplitude regions or areas may be detected byassessing the relative amplitudes of electrogram signals in step 210.

In the white areas of FIG. 7(j), the resulting velocity vector map showsthat the active rotors indicated therein are slightly moved closertogether than in FIG. 7(i), and on the left center side of FIG. 7(j) tworotors appearing in FIG. 7(i) are revealed as a single active rotor nFIG. 7(j). FIGS. 7(a) through 7(j) show that there are limits to theresolution that can be achieved using a conventional 8×8 array ofsensing electrodes 82 in a basket catheter having standardinter-electrode spacing. Thus, higher electrode densities and morerecording channels could increase the resolution and accuracy of theresults obtained using algorithm 200.

After confirming that algorithm 200 was capable of detecting complexrotor structures accurately in a patient's myocardium—even in thepresence of strong artifacts and noise—algorithm 200 was applied todifferent time portions of the actual patient data shown in FIG. 5(b) soas to test further the algorithm's efficacy and accuracy. A velocityvector map corresponding to data acquired between 4,700 milliseconds and5,100 milliseconds in the original EP recording of FIG. 5(b) is shown inFIG. 8(a).

As shown in FIG. 8(a), four rotors indicated by circles 1, 2 and 3 and astar 4 were detected. Circles 1 and 2 in FIG. 8(a) appear to denoteactive rotors that are interacting with one another. Circle (3) in FIG.8(a) may be an active rotor, but exhibits some centripetal components.Star 4 in FIG. 8(a) clearly corresponds to a passive rotor. Next, avelocity vector map corresponding to the same data set for data acquiredbetween samples 0 seconds and 400 milliseconds was generated, theresults of which are shown in FIG. 8(b). Differences between the resultsshown in FIGS. 8(a) and 8(b) permit a deeper insight into the true rotorstructure of this patient's myocardium, as best shown in FIG. 8(b). Inthe earlier time interval (0 msec. to 400 msec.) of FIG. 8(b), the twoassociated rotors 1 and 2 shown in FIG. 8(a) are not yet active, whilethere is only a single active rotor 5 in FIG. 8(b) located between thepositions of rotors 1 and 2 shown in FIG. 8(a). Rotors 1 and 2 in FIG.8(b) show up at slightly different positions, but now appear clearly aspassive rotors representing likely turbulences generated at the borderof a mitral valve artifact.

Thus, a health care professional can select differing time windows overwhich to apply algorithm 200 to an EP mapping data set as a means ofgaining a better understanding of the behavior of active and passiverotors, fibrotic regions, areas affected by valve defects or artifacts,breakthrough points and areas or defects that are at work in thepatient's myocardium. The velocity vector maps generated by algorithm200 permit a health care professional to identify such cardiac rhythmdisorders in a patient's myocardium with a degree of precision andaccuracy that has heretofore not been possible using conventional EPmapping and intravascular basket or spline catheter devices and methods.

Referring now to FIG. 9, there is shown another example of a vectorvelocity map generated from actual patient data using algorithm 200. InFIG. 9, the arrows 40 correspond to action potential wavefront velocityvectors, which as illustrated have differing magnitudes and directionsassociated herewith. As shown in FIG. 9, various cardiac rhythm defectsand disorders become apparent as a result of the generated vectorvelocity map. The defects and disorders revealed by the vector velocitymap of FIG. 9 include an active rotor (where the active rotorpropagation direction is indicated in the bottom right of FIG. 9 by acircular arrow 43 rotating in a clockwise or centrifugal direction), abreakthrough point in the bottom left of FIG. 9, fibrotic areas indictedby low-amplitude white areas in the lower portion of FIG. 9, and amitral valve defect indicted by the white area in the upper portion ofFIG. 9.

Referring now to FIGS. 10(a) through 10(d), there are shown furtherresults obtained using the actual patient data. The raw datacorresponding to FIGS. 10(a) through 10(d) were acquired from a singlepatient's right atrium using a 64-electrode basket catheter andcorresponding EP mapping/recording system. Data were acquired at a 1millisecond rate over a time period of 60 seconds in all 64 channels.FIGS. 10(a) and 10(b) correspond to one selected 2 second time window,and FIG. 10(d) corresponds to another time window from the same dataset. FIG. 10(c) shows the greyscale-schemes employed in FIGS. 10(a),10(b), and 10(d).

The vector velocity map of FIG. 10(a) generated using algorithm 200clearly reveals an active rotor located at chess board position D/E,2/3. The vector velocity map of FIG. 10(b) was also generated usingalgorithm 200, but using data acquired from only 16 electrodes 82 ingrid D-G, 2-5. As shown in FIG. 10(b), the active rotor evident in FIG.10(a) is nearly equally evident in FIG. 10(b) despite the significantlymore sparse data grid employed to produce the velocity vector map. Theseremarkable results obtained using a sparse electrode grid are due inlarge part to the robustness, stability and accuracy of algorithm 200,as it has been applied to electrographical flow problems.

FIG. 10(d) shows another example of results obtained using algorithm 200and EP mapping data obtained from the same patient as in FIGS. 10(a) and10(b), but over a different time window. Note also that FIG. 10(d) showsthat algorithm 200 has successfully detected one active rotor (at chessboard location F2/3), three active focus points, and one passive rotor(at chess board location F8).

It will now be seen that algorithm 200 provides not only rotationaldirection information, but also provides high-resolution spatialinformation regarding the presence and location of rotors despite theuse of sparse electrode grid spacing. Rotors can also move over time ina patient's myocardium, even during the time interval over which EPmapping is being carried out. The increased spatial and temporalresolution of algorithm 200 permits such shifts in rotor location to bedetected.

In some embodiments, and as described above, multiple or different typesof EP mapping and ablation catheters can be used sequentially or at thesame time to diagnose and/or treat the patient. For example, a64-electrode CONSTELLATION basket catheter can be used for EP mapping inconjunction with a PENTARAY16- or 20-electrode EP mapping catheter,where the PENTARAY EP mapping catheter is used to zero in on, andprovide fine detail regarding, a particular region of the patient'smyocardium that the basket catheter has revealed as the location of asource of a cardiac rhythm disorder or irregularity. In addition,catheter 110 or any other EP mapping catheter used in system 100 may beconfigured to provide ablation therapy (in addition to EP mappingfunctionality). The various catheters employed in system 100 may alsoinclude navigation elements, coils, markers and/or electrodes so thatthe precise positions of the sensing, pacing and/or ablation electrodesinside the patient's heart 10 are known. Navigational data can beemployed by computer 300 in algorithm 200 to provide enhanced estimatesof the locations of the electrodes in the representations, maps or gridsgenerated thereby, which in turn increases the accuracy and efficacy ofthe resulting velocity vector maps generated in algorithm 200.

In another embodiment, computing device/system 300 is operably connectedto a storage medium such as a hard drive or non-volatile memory locatedin, or operably connected to, data acquisition device 140, wherecomputing device 300 is configured to trigger an external switchoperably connected to data acquisition device 140 which permits theupload of conditioned electrogram signal data from data acquisitiondevice 140 to computing device 300. According to such a configuration,computing device 300 and data acquisition device 140 can remaingalvanically isolated from one another, and the need to physically swapUSB memory sticks between data acquisition device 140 and computingdevice 300 is eliminated. This, in turn, permits system 100 to operatemore efficiently and quickly, and to provide vector velocity maps to thehealth care professional in near-real-time while the EP mappingprocedure is being carried out within the patient's heart 10.

The various systems, devices, components and methods described anddisclosed herein may also be adapted and configured for use inelectrophysiological mapping applications other than those involving theinterior of a patient's heart. These alternative applications include EPmapping and diagnosis of a patient's epicardium, a patient's spinal cordor other nerves, or a patient's brain or portions thereof.

It will now be seen that the various systems, devices, components andmethods disclosed and described herein are capable of detecting withconsiderable accuracy and precision the locations of sources of cardiacrhythm disorders in a patient's heart.

What have been described above are examples and embodiments of thedevices and methods described and disclosed herein. It is, of course,not possible to describe every conceivable combination of components ormethodologies for purposes of describing the invention, but one ofordinary skill in the art will recognize that many further combinationsand permutations of the devices and methods described and disclosedherein are possible. Accordingly, the devices and methods described anddisclosed herein are intended to embrace all such alterations,modifications and variations that fall within the scope of the appendedclaims. In the claims, unless otherwise indicated, the article “a” is torefer to “one or more than one.”

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the detailed description setforth herein. Those skilled in the art will now understand that manydifferent permutations, combinations and variations of hearing aid 10fall within the scope of the various embodiments. Those skilled in theart should appreciate that they may readily use the present disclosureas a basis for designing or modifying other processes and structures forcarrying out the same purposes and/or achieving the same advantages ofthe embodiments introduced herein. Those skilled in the art should alsorealize that such equivalent constructions do not depart from the spiritand scope of the present disclosure, and that they may make variouschanges, substitutions and alterations herein without departing from thespirit and scope of the present disclosure.

After having read and understood the present specification, thoseskilled in the art will now understand and appreciate that the variousembodiments described herein provide solutions to long-standingproblems, both in the use of electrophysiological mapping systems and inthe use of cardiac ablation systems.

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 51. (canceled)52. A system for detecting a location of a source of at east one cardiacrhythm disorder in a patient's heart, comprising: at least one computingdevice comprising at least one non-transitory computer readable mediumconfigured to store instructions executable by at least one processorfor determining the source and location of the cardiac rhythm disorderin the patient's heart, the computing device being configured to: (a)receive electrogram signals; (b) normalize and/or adjust amplitudes ofthe electrogram signals; (c) assign predetermined positions ofelectrodes on a mapping electrode assembly to their correspondingelectrogram signals; (c) provide or generate a two-dimensional (2D)spatial map of the electrode positions; (d) for each or selecteddiscrete times over which the electrogram signals are being processed,process the amplitude-adjusted electrogram signals to generate aplurality of three-dimensional electrogram surfaces corresponding atleast partially to the 2D map, one surface being generated for each suchtime, and (e) process the plurality of three-dimensional electrogramsurfaces through time to generate a velocity vector map corresponding atleast partially to the 2D map, the velocity vector map being configuredto reveal the location of the source of the at least one cardiac rhythmdisorder.
 53. The system of claim 52, wherein at least portions of theelectrogram surfaces generated by the computing device are configured tocorrespond to estimated wave shapes or wavefront.
 54. The system ofclaim 52, wherein the electrogram surfaces are generated by thecomputing device using Green's function.
 55. The system of claim 52,wherein the electrogram surfaces are generated by the computing deviceusing a two-dimensional bi-harmonic spline interpolation function. 56.The system of claim 52, wherein the vector map generated by thecomputing device comprises arrows or colors representative of directionsof electrical potential propagation.
 57. The system of claim 52, whereinthe vector map generated by the computing device comprises arrows orcolors having attributes representative of velocities of electricalpotential propagation.
 58. The system of claim 52, wherein the vectormap generated by the computing device is configured to reveal the atleast one cardiac rhythm disorder as an active rotor at the location.59. The system of claim 52, wherein the vector map generated by thecomputing device is configured to reveal a location of a passive rotorin the patient's heart.
 60. The system of claim 52, wherein the vectormap generated by the computing device is configured to reveal a locationof a focal point in the patient's heart.
 61. The system of claim 52,wherein the vector map generated by the computing device is configuredto reveal a location of a breakthrough point in the patient's heart. 62.The system of claim 52, wherein the velocity vector map is generated bythe computing device using at least one optical flow analysis technique.63. The system of claim 62, wherein the at least one optical flowanalysis technique is selected from the group consisting of aHorn-Schunck method, a Buxton-Buston method, a Black-Jepson method, aphase correlation method, a block-based method, a discrete optimizationmethod, a Lucas-Kanade method, and a differential method of estimatingoptical flow.
 64. The system of claim 52, wherein the plurality ofelectrogram signals are processed by the computing device to generate anaveraged electrogram signal, and the averaged electrogram signal issubtracted from each of the individual electrogram signals to generateartifact- or far-field adjusted individual electrogram signals.
 65. Thesystem of claim 64, wherein the artifact-adjusted individual electrogramsignals are processed by the computing device with a high-pass filter toremove DC offsets.
 66. The system of claim 65, wherein the high-passfilter applied by the computing device removes frequencies below betweenabout 5 Hz and about 20 Hz.
 67. The system of claim 52, whereininterpolated or estimated values are generated by the computing devicefor positions in between the measured or calculated grid valuescorresponding to one or more of the electrogram signals, the pluralityof smoothed electrogram surfaces, and the velocity vector map.
 68. Thesystem of claim 52, wherein a representative amplitude value isgenerated by the computing device for each individual electrogramsignal, and the representative amplitude value generated for eachelectrogram signal is stored for later use in image backgrounds thatshow low signal amplitude areas of the 2D representation, the low signalamplitude areas being indicative of one or more of valve artifacts, poorelectrode contact, and fibrotic areas of the heart.
 69. The system ofclaim 52, wherein the electrode positions in the 2D representation aremodified by the computing device based upon navigational or positionaldata corresponding to measured or sensed actual electrode positions. 70.The system of claim 69, wherein the navigational data are provided tothe computing device by a medical navigation system, a computedtomography scanner, a magnetic resonance image scanner, or an X-rayfluoroscopy system.
 71. The system of claim 52, further comprising anelectrophysiological data acquisition device configured to receive andcondition the signals provided by the electrodes to provide as an outputtherefrom the electrogram signals.
 72. The system of claim 52, furthercomprising a screen or monitor operably connected to the computingdevice and configured to display one or more electrogram signalsreceived from the data acquisition device, the normalized oramplitude-adjusted electrogram signals, the predetermined positions ofthe electrodes on a catheter, the 2D representation of the electrodepositions, the plurality of three-dimensional smoothed electrogramsurfaces, and the velocity vector map.
 73. The system of claim 52,further comprising an ablation system comprising an ablation catheter,the ablation catheter being configured for ablating the patient's heartat the location and source of the cardiac rhythm disorder indicated bythe velocity vector map.
 74. The system of claim 52, further comprisinga catheter configured for insertion inside the patient's body and heart,the catheter comprising at a distal end thereof the mapping electrodeassembly comprising a plurality of electrodes for sensing and acquiringfrom different locations inside the patient's heart the electrogramsignals, each electrode having a predetermined position on the mappingelectrode assembly associated therewith.
 75. The system of claim 74,wherein the catheter further comprises a force sensor located at thedistal tip thereof, the force sensor being configured to engage aninterior wall of the patient's heart and indicate when the interior wallhas been engaged by the force sensor.
 76. The system of claim 52,wherein the catheter is a basket catheter.